Genetically Derived Estimates of Contemporary Natural Straying Rates and Historical Gene Flow among Lake Michigan Lake Sturgeon Populations
Bibliographic record
Abstract
Abstract Natural rates of straying are difficult to quantify over large spatial scales using direct observations, particularly for long‐lived fish species characterized by delayed sexual maturity and long interspawning intervals. Using multilocus microsatellite genotypes and likelihood‐based statistical methods, we quantified rates of immigration and emigration for six genetically differentiated (meanFST= 0.041) lake sturgeonAcipenser fulvescenspopulations in Lake Michigan based on adults (n= 437) captured in tributaries during the spawning season. Estimated rates of straying were high (mean = 0.105), asymmetrical, and highly variable across populations. We found no significant association between the total length (a surrogate measure of age) of individuals that strayed and those that did not. Linear distance between streams was more predictive of straying rates andFSTthan least‐cost distances estimated based on lakescape features (bathymetry and lake current patterns). Historical rates of gene flow estimated using coalescent analysis indicated a fully parameterized model with variable evolutionarily effective population sizes (θ range, 0.684–0.989), and variable and nonsymmetrical migration rates best explained the genetic data. Comparatively high estimates of relative historical gene flow from several numerically depressed populations suggest that these populations were once larger contributors to basinwide gene flow than indicated by estimates of contemporary straying rates. High rates of interpopulation straying contrast with highFST, suggesting that straying rates are poor indicators of successful reproduction following dispersal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".